We present a method for searching in an image database using a query image
that is similar to the intended target. The query image may be a hand-drawn
sketch or a (potentially low-quality) scan of the image to be retrieved. Our
searching algorithm makes use of multiresolution wavelet decompositions of the
query and database images. The coefficients of these decompositions are distilled
into small “signatures” for each image. We introduce an “image querying metric”
that operates on these signatures. This metric essentially compares how many
significant wavelet coefficients the query has in common with potential targets.
The metric includes parameters that can be tuned, using a statistical analysis,
to accommodate the kinds of image distortions found in different types of image
queries. The resulting algorithm is simple, requires very little storage overhead
for the database of signatures, and is fast enough to be performed on a database
of 20,000 images at interactive rates (on standard desktop machines) as a query
is sketched. Our experiments with hundreds of queries in databases of 1000 and
20,000 images show dramatic improvement, in both speed and success rate, over
using a conventional L1, L2, or color histogram norm.